Neural Network Potentials for Chemistry: Concepts, Applications and Prospects

• DOI: 10.48550/arxiv.2209.11581
• Publication Date: 2022-01-01
• Authors: Silvan Käser; Luis Itza Vazquez...; Markus Meuwly; Kai Töpfer
• Keywords: Representation

Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks the field of computational chemistry such as representation potential energy surfaces (PES) spectroscopic predictions. This perspective provides an overview foundations neural network-based full-dimensional surfaces, their architectures, underlying concepts, to chemical systems. Methods data generation training procedures PES construction discussed means error assessment refinement through transfer learning presented. A selection recent results illustrates latest improvements regarding accuracy representations system size limitations dynamics simulations, but also NN application enabling direct prediction physical without simulations. The aim is provide current state-of-the-art approaches point out challenges enhancing reliability applicability on larger scale.